# A Parallel SGD method with Strong Convergence

arXiv: https://arxiv.org/abs/1311.0636 · HTML: https://mlsanity.com/paper/1311.0636/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2013.

## Built on by (most cited first, 2 of 2)

- [Federated Empirical Risk Minimization via Second-Order Method](https://mlsanity.com/paper/2305.17482/) (2023)
- [Learning Linear Models Using Distributed Iterative Hessian Sketching](https://mlsanity.com/paper/2112.04101/) (2021)

## Abstract (arXiv)

This paper proposes a novel parallel stochastic gradient descent (SGD) method that is obtained by applying parallel sets of SGD iterations (each set operating on one node using the data residing in it) for finding the direction in each iteration of a batch descent method. The method has strong convergence properties. Experiments on datasets with high dimensional feature spaces show the value of this method.